Editing Data warehouse (section)

===Hybrid design===
Data warehouses often resemble the [[hub and spokes architecture]]. [[Legacy system]]s feeding the warehouse often include [[customer relationship management]] and [[enterprise resource planning]], generating large amounts of data. To consolidate these various data models, and facilitate the [[extract transform load]] process, data warehouses often make use of an [[operational data store]], the information from which is parsed into the actual data warehouse. To reduce data redundancy, larger systems often store the data in a normalized way. Data marts for specific reports can then be built on top of the data warehouse.

A hybrid (also called ensemble) data warehouse database is kept on [[third normal form]] to eliminate [[data redundancy]]. A normal relational database, however, is not efficient for business intelligence reports where dimensional modelling is prevalent. Small data marts can shop for data from the consolidated warehouse and use the filtered, specific data for the fact tables and dimensions required. The data warehouse provides a single source of information from which the data marts can read, providing a wide range of business information. The hybrid architecture allows a data warehouse to be replaced with a [[master data management]] repository where operational (not static) information could reside.

The [[data vault modeling]] components follow hub and spokes architecture. This modeling style is a hybrid design, consisting of the best practices from both third normal form and [[star schema]]. The data vault model is not a true third normal form, and breaks some of its rules, but it is a top-down architecture with a bottom up design. The data vault model is geared to be strictly a data warehouse. It is not geared to be end-user accessible, which, when built, still requires the use of a data mart or star schema-based release area for business purposes.